#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
"""Encoder self-attention layer definition."""

import torch
import torch.nn.functional as F
from torch import nn
from typeguard import check_argument_types
from typing import Optional, Tuple, List

from wenet.models.moe_comformer.attention import RelPositionMultiHeadedAttention
from wenet.models.moe_comformer.epoch_based_activator import (simple_factory,
                                                       EpochBasedActivator)


class MultiSourceMixtureOfEncodings(torch.nn.Module):
    def __init__(self, dim: int, num_src: int, prob=0, **kwargs):
        assert check_argument_types()
        super().__init__()
        self.prob = simple_factory(EpochBasedActivator, prob)
        if self.prob.possible():
            self.cross_attn = torch.nn.ModuleList(
                [RelPositionMultiHeadedAttention(int(dim / 64), dim, 0)
                 for _ in range(num_src)])
            self.fc = torch.nn.Linear(dim * (num_src + 1), num_src + 1)

    def set_epoch(self, epoch: int):
        self.prob.set_epoch(epoch)

    def forward(self, xs, src: list, masks, pos_emb):
        if not self.prob.activated():
            return xs;  # does nothing.
        B, T, D = xs.shape
        xs_atts = [xs]
        for i, ca in enumerate(self.cross_attn):
            xs_atts.append(ca(xs, src[i], src[i], masks, pos_emb))
        xs_concat = torch.cat(xs_atts, dim=-1)
        gating = F.softmax(self.fc(xs_concat), dim=-1).unsqueeze(-1)
        return torch.sum(gating * xs_concat.view(B, T, -1, D), dim=2)


class TransformerEncoderLayer(nn.Module):
    """Encoder layer module.

    Args:
        size (int): Input dimension.
        self_attn (torch.nn.Module): Self-attention module instance.
            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
            instance can be used as the argument.
        feed_forward (torch.nn.Module): Feed-forward module instance.
            `PositionwiseFeedForward`, instance can be used as the argument.
        dropout_rate (float): Dropout rate.
        normalize_before (bool):
            True: use layer_norm before each sub-block.
            False: to use layer_norm after each sub-block.
        concat_after (bool): Whether to concat attention layer's input and
            output.
            True: x -> x + linear(concat(x, att(x)))
            False: x -> x + att(x)

    """
    def __init__(
        self,
        size: int,
        self_attn: torch.nn.Module,
        feed_forward: torch.nn.Module,
        dropout_rate: float,
        normalize_before: bool = True,
        concat_after: bool = False,
        **kwargs,
    ):
        """Construct an EncoderLayer object."""
        super().__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.norm1 = nn.LayerNorm(size, eps=1e-5)
        self.norm2 = nn.LayerNorm(size, eps=1e-5)
        self.dropout = nn.Dropout(dropout_rate)
        self.size = size
        self.normalize_before = normalize_before
        self.concat_after = concat_after
        if concat_after:
            self.concat_linear = nn.Linear(size + size, size)

    def set_epoch(self, epoch):
        if hasattr(self.feed_forward, 'set_epoch'):
            self.feed_forward.set_epoch(epoch)

    def forward(
        self,
        x: torch.Tensor,
        mask: torch.Tensor,
        pos_emb: torch.Tensor,
        mask_pad: Optional[torch.Tensor] = None,
        output_cache: Optional[torch.Tensor] = None,
        cnn_cache: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Compute encoded features.

        Args:
            x (torch.Tensor): Input tensor (#batch, time, size).
            mask (torch.Tensor): Mask tensor for the input (#batch, time).
            pos_emb (torch.Tensor): just for interface compatibility
                to ConformerEncoderLayer
            mask_pad (torch.Tensor): does not used in moe_comformer layer,
                just for unified api with conformer.
            output_cache (torch.Tensor): Cache tensor of the output
                (#batch, time2, size), time2 < time in x.
            cnn_cache (torch.Tensor): not used here, it's for interface
                compatibility to ConformerEncoderLayer
        Returns:
            torch.Tensor: Output tensor (#batch, time, size).
            torch.Tensor: Mask tensor (#batch, time).

        """
        residual = x
        if self.normalize_before:
            x = self.norm1(x)

        if output_cache is None:
            x_q = x
        else:
            assert output_cache.size(0) == x.size(0)
            assert output_cache.size(2) == self.size
            assert output_cache.size(1) < x.size(1)
            chunk = x.size(1) - output_cache.size(1)
            x_q = x[:, -chunk:, :]
            residual = residual[:, -chunk:, :]
            mask = mask[:, -chunk:, :]

        x_att = self.self_attn(x_q, x, x, mask, pos_emb)
        if self.concat_after:
            x_concat = torch.cat((x, x_att), dim=-1)
            x = residual + self.concat_linear(x_concat)
        else:
            x = residual + self.dropout(x_att)
        if not self.normalize_before:
            x = self.norm1(x)

        residual = x
        if self.normalize_before:
            x = self.norm2(x)
        x = residual + self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm2(x)

        if output_cache is not None:
            x = torch.cat([output_cache, x], dim=1)

        return x, mask, None


class ConformerEncoderLayer(nn.Module):
    """Encoder layer module.
    Args:
        size (int): Input dimension.
        self_attn (torch.nn.Module): Self-attention module instance.
            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
            instance can be used as the argument.
        feed_forward (torch.nn.Module): Feed-forward module instance.
            `PositionwiseFeedForward` instance can be used as the argument.
        feed_forward_macaron (torch.nn.Module): Additional feed-forward module
             instance.
            `PositionwiseFeedForward` instance can be used as the argument.
        conv_module (torch.nn.Module): Convolution module instance.
            `ConvlutionModule` instance can be used as the argument.
        dropout_rate (float): Dropout rate.
        normalize_before (bool):
            True: use layer_norm before each sub-block.
            False: use layer_norm after each sub-block.
        concat_after (bool): Whether to concat attention layer's input and
            output.
            True: x -> x + linear(concat(x, att(x)))
            False: x -> x + att(x)
    """
    def __init__(
        self,
        size: int,
        self_attn: torch.nn.Module,
        feed_forward: Optional[nn.Module] = None,
        feed_forward_macaron: Optional[nn.Module] = None,
        conv_module: Optional[nn.Module] = None,
        dropout_rate: float = 0.1,
        normalize_before: bool = True,
        concat_after: bool = False,
        moe_size: int = 0,
        moe_prob=0,
        hyp_attn: torch.nn.Module = None,
    ):
        """Construct an EncoderLayer object."""
        super().__init__()
        self.size = size
        self.normalize_before = normalize_before
        # macaron ff block
        if feed_forward_macaron:
            if normalize_before:
                self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5)
            self.feed_forward_macaron = feed_forward_macaron
            if not normalize_before:
                self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5)
            self.ff_scale = 0.5
        else:
            self.feed_forward_macaron = None
            self.ff_scale = 1.0
        # attention block
        if normalize_before:
            self.norm_mha = nn.LayerNorm(size, eps=1e-5)
        self.self_attn = self_attn
        self.concat_after = concat_after
        if concat_after:
            self.concat_linear = nn.Linear(size + size, size)
        if not normalize_before:
            self.norm_mha = nn.LayerNorm(size, eps=1e-5)
        # convolution block
        if conv_module:
            if normalize_before:
                self.norm_conv = nn.LayerNorm(size, eps=1e-5)
            self.conv_module = conv_module
            if not normalize_before:
                self.norm_conv = nn.LayerNorm(size, eps=1e-5)
        else:
            self.conv_module = None
        # ff block
        if normalize_before:
            self.norm_ff = nn.LayerNorm(size, eps=1e-5)
        self.feed_forward = feed_forward
        if not normalize_before:
            self.norm_ff = nn.LayerNorm(size, eps=1e-5)
        # final norm if there is CNN.
        if conv_module:
            self.norm_final = nn.LayerNorm(
                size, eps=1e-5)  # for the final output of the block
        # Multi-source MOE
        self.msmoe = MultiSourceMixtureOfEncodings(
            size, moe_size, moe_prob) if moe_size > 0 else None
        self.dropout = nn.Dropout(dropout_rate)

    def set_epoch(self, epoch):
        if self.msmoe:
            self.msmoe.set_epoch(epoch)
        if (self.feed_forward_macaron
            and hasattr(self.feed_forward_macaron, 'set_epoch')):
            self.feed_forward_macaron.set_epoch(epoch)
        if hasattr(self.feed_forward, 'set_epoch'):
            self.feed_forward.set_epoch(epoch)

    def get_debug_stat(self, infos: list, name=''):
        if (self.feed_forward_macaron
            and hasattr(self.feed_forward_macaron, 'get_debug_stat')):
            self.feed_forward_macaron.get_debug_stat(infos, f'{name}.ff_macaron')
        if hasattr(self.feed_forward, 'get_debug_stat'):
            self.feed_forward.get_debug_stat(infos, f'{name}.ff')

    def forward(
        self,
        x: torch.Tensor,
        mask: torch.Tensor,
        pos_emb: torch.Tensor,
        mask_pad: Optional[torch.Tensor] = None,
        output_cache: Optional[torch.Tensor] = None,
        cnn_cache: Optional[torch.Tensor] = None,
        enc_splice: Optional[List[torch.Tensor]] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Compute encoded features.

        Args:
            x (torch.Tensor): (#batch, time, size)
            mask (torch.Tensor): Mask tensor for the input (#batch, time，time).
            pos_emb (torch.Tensor): positional encoding, must not be None
                for ConformerEncoderLayer.
            mask_pad (torch.Tensor): batch padding mask used for conv module.
                (#batch, 1，time)
            output_cache (torch.Tensor): Cache tensor of the output
                (#batch, time2, size), time2 < time in x.
            cnn_cache (torch.Tensor): Convolution cache in conformer layer
        Returns:
            torch.Tensor: Output tensor (#batch, time, size).
            torch.Tensor: Mask tensor (#batch, time).
        """

        # whether to use macaron style
        if self.feed_forward_macaron is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_ff_macaron(x)
            x = residual + self.ff_scale * self.dropout(
                self.feed_forward_macaron(x))
            if not self.normalize_before:
                x = self.norm_ff_macaron(x)

        # multi-headed self-attention module
        residual = x
        if self.normalize_before:
            x = self.norm_mha(x)

        if output_cache is None:
            x_q = x
        else:
            assert output_cache.size(0) == x.size(0)
            assert output_cache.size(2) == self.size
            assert output_cache.size(1) < x.size(1)
            chunk = x.size(1) - output_cache.size(1)
            x_q = x[:, -chunk:, :]
            residual = residual[:, -chunk:, :]
            mask = mask[:, -chunk:, :]

        x_att = self.self_attn(x_q, x, x, mask, pos_emb)
        if self.concat_after:
            x_concat = torch.cat((x, x_att), dim=-1)
            x = residual + self.concat_linear(x_concat)
        else:
            x = residual + self.dropout(x_att)
        if not self.normalize_before:
            x = self.norm_mha(x)

        # convolution module
        # Fake new cnn cache here, and then change it in conv_module
        new_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device)
        if self.conv_module is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_conv(x)
            x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
            x = residual + self.dropout(x)

            if not self.normalize_before:
                x = self.norm_conv(x)

        # feed forward module
        residual = x
        if self.normalize_before:
            x = self.norm_ff(x)

        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm_ff(x)

        if self.conv_module is not None:
            x = self.norm_final(x)

        if output_cache is not None:
            x = torch.cat([output_cache, x], dim=1)

        if self.msmoe and enc_splice:
            x = self.msmoe(x, enc_splice, mask, pos_emb)

        return x, mask, new_cnn_cache

    def forward_incremental(
        self,
        x: torch.Tensor,
        pos_emb: torch.Tensor,
        att_mask: torch.Tensor,
        pad_mask: torch.Tensor,
        att_cache: torch.Tensor,
        cnn_cache: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        # whether to use macaron style
        if self.feed_forward_macaron is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_ff_macaron(x)
            x = residual + self.ff_scale * self.dropout(
                self.feed_forward_macaron(x))
            if not self.normalize_before:
                x = self.norm_ff_macaron(x)

        # multi-headed self-attention module
        residual = x
        if self.normalize_before:
            x = self.norm_mha(x)

        att_cache = att_cache.reshape(x.size(0), -1, self.size)
        new_att_cache = x
        x_kv = torch.cat((att_cache, x), dim=1)
        x_att = self.self_attn(x, x_kv, x_kv, att_mask, pos_emb)

        if self.concat_after:
            x_concat = torch.cat((x, x_att), dim=-1)
            x = residual + self.concat_linear(x_concat)
        else:
            x = residual + self.dropout(x_att)
        if not self.normalize_before:
            x = self.norm_mha(x)

        # convolution module
        # Fake new cnn cache here, and then change it in conv_module
        new_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device)
        if self.conv_module is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_conv(x)
            x, new_cnn_cache = self.conv_module(x, pad_mask, cnn_cache)
            x = residual + self.dropout(x)
            if not self.normalize_before:
                x = self.norm_conv(x)

        # feed forward module
        residual = x
        if self.normalize_before:
            x = self.norm_ff(x)
        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm_ff(x)

        if self.conv_module is not None:
            x = self.norm_final(x)

        return x, new_att_cache, new_cnn_cache


class ConformerEncoderDeepnormLayer(nn.Module):
    """Encoder layer module.
    Args:
        size (int): Input dimension.
        self_attn (torch.nn.Module): Self-attention module instance.
            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
            instance can be used as the argument.
        feed_forward (torch.nn.Module): Feed-forward module instance.
            `PositionwiseFeedForward` instance can be used as the argument.
        feed_forward_macaron (torch.nn.Module): Additional feed-forward module
             instance.
            `PositionwiseFeedForward` instance can be used as the argument.
        conv_module (torch.nn.Module): Convolution module instance.
            `ConvlutionModule` instance can be used as the argument.
        dropout_rate (float): Dropout rate.
    """
    def __init__(
        self,
        size: int,
        alpha: float,
        beta: float,
        self_attn: torch.nn.Module,
        pre_feed_forward: torch.nn.Module,
        post_feed_forward: torch.nn.Module,
        conv_module: torch.nn.Module,
        dropout_rate: float = 0.1,
    ):
        """Construct an EncoderLayer object."""
        super().__init__()
        self.size = size
        self.alpha = alpha
        self.beta = beta
        self.attn = self_attn
        self.ff1 = pre_feed_forward
        self.ff2 = post_feed_forward
        self.conv = conv_module
        self.norm_ff1 = nn.LayerNorm(size, eps=1e-5)  # for the FNN module
        self.norm_attn = nn.LayerNorm(size, eps=1e-5)  # for the MHA module
        self.norm_conv = nn.LayerNorm(size, eps=1e-5)  # for the CNN module
        self.norm_ff2 = nn.LayerNorm(size, eps=1e-5)  # for the FNN module
        self.dropout = nn.Dropout(dropout_rate)
        self.reset_parameters()

    def reset_parameters(self):
        self.ff1.reset_parameters()
        self.ff2.reset_parameters()
        self.attn.reset_parameters()
        for name, p in self.named_parameters():
            if ('w_1' in name
                or 'w_2' in name
                or 'linear_v' in name
                or 'linear_out' in name):
                p.data.div_(self.beta)

    def set_epoch(self, epoch):
        pass

    def forward(
        self,
        x: torch.Tensor,
        mask: torch.Tensor,
        pos_emb: torch.Tensor,
        mask_pad: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Compute encoded features.
        Args:
            x (torch.Tensor): (#batch, time, size)
            mask (torch.Tensor): Mask tensor for the input (#batch, time，time).
            pos_emb (torch.Tensor): positional encoding, must not be None
                for ConformerEncoderLayer.
            mask_pad (torch.Tensor): batch padding mask used for conv module.
                (#batch, 1，time)
            cnn_cache (torch.Tensor): Convolution cache in conformer layer
        Returns:
            torch.Tensor: Output tensor (#batch, time, size).
            torch.Tensor: Mask tensor (#batch, time).
        """
        x = self.norm_ff1(self.alpha * x + 0.5 * self.dropout(self.ff1(x)))
        x_attn = self.attn(x, x, x, mask, pos_emb)
        x = self.norm_attn(self.alpha * x + self.dropout(x_attn))
        x_conv, _ = self.conv(x, mask_pad)
        x = self.norm_conv(self.alpha * x + self.dropout(x_conv))
        x = self.norm_ff2(self.alpha * x + 0.5 * self.dropout(self.ff2(x)))
        return x, mask, None  # third return value for compatibility
